The disclosure of magnetic ionic liquids (MILs) as stable dispersions of surface modified γ-Fe2O3 or CoFe2O4 nanoparticles (NPs) in the 1-n-butyl-3-methylimidazolium tetrafluoroborate (BMIBF4) ionic liquid is reported. The magnetic NPs were characterized by X-ray powder diffraction, transmission electron microscopy, and Raman spectroscopy. The surface modified NPs have proved to form stable dispersions in BMIBF4 in the absence of water and behave like a magnetic ionic liquid. The MILs have been characterized by Raman spectroscopy, magnetic measurements, and DSC. The stability of the magnetic NPs in BMIBF4 is consistently explained by assuming the formation of a semiorganized protective layer composed of supramolecular aggregates in the form of [(BMI)2(BF4)3]−. A superparamagnetic behavior and saturation magnetization of ca. 18 emu/g for a sample containing 30% w/w maghemite NPs/BMIBF4 have been inferred from static and dynamic magnetic measurements. DSC results have shown that the MIL composed of 30% w/w CoFe2O4 NPs/BMIBF4 remains a liquid phase down to −84 °C.

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Three different calibration curves based on (1)H-NMR spectroscopy (300 MHz) were used for quantifying the reaction yield during biodiesel synthesis by esterification of fatty acids mixtures and methanol. For this purpose, the integrated intensities of the hydrogens of the ester methoxy group (3.67 ppm) were correlated with the areas related to the various protons of the alkyl chain (olefinic hydrogens: 5.30-5.46 ppm; aliphatic: 2.67-2.78 ppm, 2.30 ppm, 1.96-2.12 ppm, 1.56-1.68 ppm, 1.22-1.42 ppm, 0.98 ppm, and 0.84-0.92 ppm). The first curve was obtained using the peaks relating the olefinic hydrogens, a second with the parafinic protons and the third curve using the integrated intensities of all the hydrogens. A total of 35 samples were examined: 25 samples to build the three different calibration curves and ten samples to serve as external validation samples. The results showed no statistical differences among the three methods, and all presented prediction errors less than 2.45% with a co-efficient of variation (CV) of 4.66%.

The use of ethanol fuel is becoming worldwide and Brazil is the largest consumer of ethanol fuel as hydrated ethyl alcohol fuel (AEHC). Due to the similarities of the physical−chemical properties of ethanol and methanol, ethanol fuel is being adulterated with methanol. In the present work, we propose the use of partial least-squares regression (PLS) calibration models based on Fourier transform mid-infrared (FTMIR) and Fourier transform near-infrared (FTNIR) measurements as a fast, precise, and accurate method to evaluate the quality of AEHC as well as to detect its adulteration with methanol. Eighty mixtures of methanol/ethanol/water standards were prepared. Sixty were used for calibration, and twenty, for validation. The validation samples were also analyzed by gas chromatography flame ionization detection (GC-FID) to determine the ethanol and methanol content. The results have shown that among the two investigated spectroscopic techniques, PLS/FTNIR presented the best performance for the detection of methanol with a root-mean-square error of prediction (RMSEP % w/w) of 0.15 as compared to the value of 0.54 obtained by the FTMIR model, while the GC-FID results presented a prediction error of 0.52 (% w/w). The minimum detected net concentration of MeOH with the FTNIR model was ca. 0.51 (% w/w).

Recebido em 6/3/03; aceito em 29/9/03 THE CHOICE OF THE SPECTRAL REGION IN THE USE OF SPECTROSCOPIC AND CHEMOMETRIC METHODS. A method is presented for the choice of spectral regions when absorption measurements are coupled to chemometric tools to perform quantitative analyses. The method is based on the spectral distribution of the relative standard deviation of concentration (s c /c).It has been applied to the development of PLS-FTNIR calibration models for the determination of density and MON of gasoline, and ethanol content and density of ethanol fuel. The new method was also compared with the correlation (R 2 ) method and has proved to generate PLS calibration models that present better accuracy and precision than those based on R 2 . Keyword: FTNIR; PLS; spectral region selection. INTRODUÇÃOO uso da espectroscopia de absorção no infravermelho próximo (NIR, "near infrared"), combinada com o tratamento dos dados por métodos quimiométricos, tornou-se rotineiro, encontrando aplicações na classificação de restos de demolição 1 , no monitoramento "on line" de processos de polimerização 2 , na análise de fármacos 3-5 , solos 6,7 e alimentos 8 , na agro-indústria 8,9 , na indústria têxtil 10 , em refinarias 11,12 , além de várias outras aplicações. Dentre os métodos quimiométricos que têm sido propostos para a análise espectral de multicomponentes, os mais populares são o PCR ("principal component regression") 13 , o PLS ("partial lastquares") 14 e seus análogos [15][16][17][18][19] . Com base em algumas suposições, conseguiu-se demonstrar, teoricamente, que a inclusão de mais regiões espectrais sempre melhora a eficiência de previsão dos modelos baseados em análise multivariada 20 . Como bem colocado por Jiang et al. 21 , a conseqüência direta deste tipo de suposição sugere que tais métodos podem levar à construção de modelos de calibração usando espectros inteiros, sem a necessidade de seleção de regiões espectrais específicas. No entanto, evidências, tanto teóricas 22,23 como experimentais 24,25 , indicam que a escolha das regiões espectrais pode melhorar significativamente a eficiência destas técnicas de calibração.A escolha da região espectral envolve a escolha de determinados canais espectrais (um comprimento de onda ou um conjunto de comprimentos de onda), que permitem ao modelo de calibração minimizar os erros de previsão. Os benefícios da seleção da região espectral vão desde a estabilidade do modelo de calibração em relação à colinearidade, como também na interpretação das relações entre o modelo e a composição da amostra 21 . Existem vários procedimentos para a escolha da região espectral. Alguns baseiam-se em critérios objetivos para a avaliação do melhor conjunto de canais espectrais, enquanto outros se baseiam em algoritmos que localizam o conjunto de canais espectrais que podem dar os melhores resultados. Como exemplos de critérios objetivos podemos citar a análise da distribuição espectral da relação sinalruído, do determinante da matriz de calibração, bem como a avaliação da raiz quadrada dos erros...

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